describer

describer quickly and easily describes data using common descriptive statistics.

Installation

You can install the latest development version from CRAN:

install.packages("describer")

Or from GitHub with:

if (packageVersion("devtools") < 1.6) {
  install.packages("devtools")
}
devtools::install_github("paulhendricks/describer")

If you encounter a clear bug, please file a minimal reproducible example on GitHub.

API

library(dplyr, warn.conflicts = FALSE)
library(describer)

mtcars %>% 
  describe %>% 
  knitr::kable(format = "markdown")

.column_name	.count_elements	.mean_value	.sd_value	.q0_value	.q25_value	.q50_value	.q75_value	.q100_value
mpg	32	20.090625	6.0269481	10.400	15.42500	19.200	22.80	33.900
cyl	32	6.187500	1.7859216	4.000	4.00000	6.000	8.00	8.000
disp	32	230.721875	123.9386938	71.100	120.82500	196.300	326.00	472.000
hp	32	146.687500	68.5628685	52.000	96.50000	123.000	180.00	335.000
drat	32	3.596563	0.5346787	2.760	3.08000	3.695	3.92	4.930
wt	32	3.217250	0.9784574	1.513	2.58125	3.325	3.61	5.424
qsec	32	17.848750	1.7869432	14.500	16.89250	17.710	18.90	22.900
vs	32	0.437500	0.5040161	0.000	0.00000	0.000	1.00	1.000
am	32	0.406250	0.4989909	0.000	0.00000	0.000	1.00	1.000
gear	32	3.687500	0.7378041	3.000	3.00000	4.000	4.00	5.000
carb	32	2.812500	1.6152000	1.000	2.00000	2.000	4.00	8.000

mtcars %>% 
  group_by(cyl) %>% 
  do(describe(.)) %>% 
  knitr::kable(format = "markdown")

cyl	.column_name	.count_elements	.mean_value	.sd_value	.q0_value	.q25_value	.q50_value	.q75_value	.q100_value
4	mpg	11	26.6636364	4.5098277	21.400	22.8000	26.000	30.40000	33.900
4	cyl	11	4.0000000	0.0000000	4.000	4.0000	4.000	4.00000	4.000
4	disp	11	105.1363636	26.8715937	71.100	78.8500	108.000	120.65000	146.700
4	hp	11	82.6363636	20.9345300	52.000	65.5000	91.000	96.00000	113.000
4	drat	11	4.0709091	0.3654711	3.690	3.8100	4.080	4.16500	4.930
4	wt	11	2.2857273	0.5695637	1.513	1.8850	2.200	2.62250	3.190
4	qsec	11	19.1372727	1.6824452	16.700	18.5600	18.900	19.95000	22.900
4	vs	11	0.9090909	0.3015113	0.000	1.0000	1.000	1.00000	1.000
4	am	11	0.7272727	0.4670994	0.000	0.5000	1.000	1.00000	1.000
4	gear	11	4.0909091	0.5393599	3.000	4.0000	4.000	4.00000	5.000
4	carb	11	1.5454545	0.5222330	1.000	1.0000	2.000	2.00000	2.000
6	mpg	7	19.7428571	1.4535670	17.800	18.6500	19.700	21.00000	21.400
6	cyl	7	6.0000000	0.0000000	6.000	6.0000	6.000	6.00000	6.000
6	disp	7	183.3142857	41.5624602	145.000	160.0000	167.600	196.30000	258.000
6	hp	7	122.2857143	24.2604911	105.000	110.0000	110.000	123.00000	175.000
6	drat	7	3.5857143	0.4760552	2.760	3.3500	3.900	3.91000	3.920
6	wt	7	3.1171429	0.3563455	2.620	2.8225	3.215	3.44000	3.460
6	qsec	7	17.9771429	1.7068657	15.500	16.7400	18.300	19.17000	20.220
6	vs	7	0.5714286	0.5345225	0.000	0.0000	1.000	1.00000	1.000
6	am	7	0.4285714	0.5345225	0.000	0.0000	0.000	1.00000	1.000
6	gear	7	3.8571429	0.6900656	3.000	3.5000	4.000	4.00000	5.000
6	carb	7	3.4285714	1.8126539	1.000	2.5000	4.000	4.00000	6.000
8	mpg	14	15.1000000	2.5600481	10.400	14.4000	15.200	16.25000	19.200
8	cyl	14	8.0000000	0.0000000	8.000	8.0000	8.000	8.00000	8.000
8	disp	14	353.1000000	67.7713236	275.800	301.7500	350.500	390.00000	472.000
8	hp	14	209.2142857	50.9768855	150.000	176.2500	192.500	241.25000	335.000
8	drat	14	3.2292857	0.3723618	2.760	3.0700	3.115	3.22500	4.220
8	wt	14	3.9992143	0.7594047	3.170	3.5325	3.755	4.01375	5.424
8	qsec	14	16.7721429	1.1960138	14.500	16.0975	17.175	17.55500	18.000
8	vs	14	0.0000000	0.0000000	0.000	0.0000	0.000	0.00000	0.000
8	am	14	0.1428571	0.3631365	0.000	0.0000	0.000	0.00000	1.000
8	gear	14	3.2857143	0.7262730	3.000	3.0000	3.000	3.00000	5.000
8	carb	14	3.5000000	1.5566236	2.000	2.2500	3.500	4.00000	8.000

To mimic the exact pandas.describe() behavior, use pd_describe.

library(reshape2)

pandas_describe_mtcars <- 
  mtcars %>% 
  pd_describe

pandas_describe_mtcars %>% 
  knitr::kable(format = "markdown")

.variable	am	carb	cyl	disp	drat	gear	hp	mpg	qsec	vs	wt
.count_elements	32.0000000	32.0000	32.000000	32.0000	32.0000000	32.0000000	32.00000	32.000000	32.000000	32.0000000	32.0000000
.mean_value	0.4062500	2.8125	6.187500	230.7219	3.5965625	3.6875000	146.68750	20.090625	17.848750	0.4375000	3.2172500
.sd_value	0.4989909	1.6152	1.785922	123.9387	0.5346787	0.7378041	68.56287	6.026948	1.786943	0.5040161	0.9784574
.q0_value	0.0000000	1.0000	4.000000	71.1000	2.7600000	3.0000000	52.00000	10.400000	14.500000	0.0000000	1.5130000
.q25_value	0.0000000	2.0000	4.000000	120.8250	3.0800000	3.0000000	96.50000	15.425000	16.892500	0.0000000	2.5812500
.q50_value	0.0000000	2.0000	6.000000	196.3000	3.6950000	4.0000000	123.00000	19.200000	17.710000	0.0000000	3.3250000
.q75_value	1.0000000	4.0000	8.000000	326.0000	3.9200000	4.0000000	180.00000	22.800000	18.900000	1.0000000	3.6100000
.q100_value	1.0000000	8.0000	8.000000	472.0000	4.9300000	5.0000000	335.00000	33.900000	22.900000	1.0000000	5.4240000

str(pandas_describe_mtcars)
#> 'data.frame':    8 obs. of  12 variables:
#>  $ .variable: chr  ".count_elements" ".mean_value" ".sd_value" ".q0_value" ...
#>  $ am       : num  32 0.406 0.499 0 0 ...
#>  $ carb     : num  32 2.81 1.62 1 2 ...
#>  $ cyl      : num  32 6.19 1.79 4 4 ...
#>  $ disp     : num  32 230.7 123.9 71.1 120.8 ...
#>  $ drat     : num  32 3.597 0.535 2.76 3.08 ...
#>  $ gear     : num  32 3.688 0.738 3 3 ...
#>  $ hp       : num  32 146.7 68.6 52 96.5 ...
#>  $ mpg      : num  32 20.09 6.03 10.4 15.43 ...
#>  $ qsec     : num  32 17.85 1.79 14.5 16.89 ...
#>  $ vs       : num  32 0.438 0.504 0 0 ...
#>  $ wt       : num  32 3.217 0.978 1.513 2.581 ...

Citation

To cite package ‘describer’ in publications use:

Paul Hendricks (2015). describer: Describe Data in R Using Common Descriptive Statistics. R package version 0.2.0. https://CRAN.R-project.org/package=describer

A BibTeX entry for LaTeX users is

@Manual{,
  title = {describer: Describe Data in R Using Common Descriptive Statistics},
  author = {Paul Hendricks},
  year = {2015},
  note = {R package version 0.2.0},
  url = {https://CRAN.R-project.org/package=describer},
}